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Post-Conversation Analysis allows your agent to analyze conversations after they end and extract useful insights automatically. These insights convert conversations into structured data that can be stored, analyzed, or sent to other systems. You can define multiple fields and instruct the AI to extract specific information from each conversation. These Analysis convert conversations into usable data that can be:
  • Stored
  • Analyzed
Sent to external systems
👉 Post-Conversation Analysis is billed separately. Charges are based on the chat message pricing of the selected LLM model.

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Enable Post-Conversation Analysis

To enable and configure Post-Conversation Analysis:
  1. Open your Agent Builder.
  2. From the side panel, click Agent Settings.
  3. Expand the Post-Conversation Analysis section.
  4. Toggle the switch ON to enable the analysis
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Once enabled, the selected model (for example GPT-4o mini) will analyze conversations after they end and extract the configured Analysis

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Add New Field

To extract specific information from conversations, you can create custom fields.
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  1. In the Post-Conversation Analysis section, click Add New Field.
  2. Select the type of Field you want to create.
  3. Enter an Name of Analysis.
  4. Provide Analysis Instructions that tell the AI what to extract from the conversation.
  5. Click Submit.
The AI will analyze each conversation and populate the field based on your instructions.

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Field Types

SigmaMind supports four types of fields.

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Text

The Text field is used when you want the AI to generate written output.

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Best For:

Conversation summaries
Main reason for the call
Key discussion points
Example 
Analysis Name:
conversation summary
Analysis Instructions: 
Write a concise summary of the conversation including the main topic and final outcome.

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Selector

The Selector field allows the AI to choose one value from predefined options. This is useful for categorizing conversations into structured outcomes.

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Best For:

Call outcomes
Intent classification
Routing analysis
Example 
Analysis Name:
call outcome
Analysis Instructions 
Select the final outcome of the conversation from the predefined options.

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Boolean

The Boolean field returns a true or false value. This is useful for determining whether a specific event occurred during the conversation.

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Best For:

Event tracking
Conversion tracking
Example 
Analysis Name:
appointment booked
Analysis Instructions 
Return true if an appointment was booked during the conversation, otherwise return false.

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Number

The Number field allows the AI to return a numeric value. This is useful for scoring or counting information from the conversation.

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Best For:

Scoring
Counting
Ratings
Example 
Analysis Name:
sentiment score
Analysis Instructions 
Rate the overall customer sentiment from 1 to 10 where 1 is very negative and 10 is very positive.
Choosing the Right Model You can select which LLM model to use for Post-Conversation Analysis. For Simple Classification (Fast & Low Cost)
Use:
  • GPT-4o mini
Best for:
  • Boolean fields (true/false)
  • Selector fields (categorization)
Basic tagging (intent, outcome) Performance:
  • ⚡ Fastest response time
  • 💲 Lowest cost
  • 📉 Lower depth of analysis
For Balanced Performance (Most Common Use Cases)
Use:
  • GPT-4o
Best for:
  • Conversation summaries
  • Key information extraction
  • General analysis with good accuracy
Performance:
  • ⚖️ Balanced latency and quality
  • 💲 Moderate cost
  • ✅ Recommended default for most users
For Advanced Analysis (High Accuracy)
Use:
  • GPT-5 (or latest high-quality model available)
Best for:
  • Complex reasoning
  • Detailed summaries
  • Multi-step analysis
  • High-stakes workflows (sales insights, compliance, etc.)
Performance:
  • 🧠 Highest quality output
  • 🐢 Higher latency (slower responses)
  • 💲 Higher cost

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Trade-offs

  • GPT-4o mini → Lowest cost, fastest, but less detailed
  • GPT-4o → Balanced cost and quality (recommended default)
  • GPT-5 → Highest quality, but higher cost and latency

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Recommendation

  • Start with GPT-4o for most use cases
  • Use GPT-4o mini for simple structured fields
  • Upgrade to GPT-5 only when higher accuracy is required

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Managing Fields

After creating a field, you can:
  • Edit the field to update the instructions
  • Delete the field if it is no longer required
  • Add multiple fields to capture different insights from conversations
You can also choose which model should be used for the analysis.

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Sending Extracted Data

The Analysisextracted through Post-Conversation Analysis can be sent to external systems using SigmaMind Webhooks. Using webhooks, you can automatically send the extracted data to:
  • CRM systems
  • Google Sheets
  • Databases
  • Automation platforms such as n8n or Zapier
  • Custom applications
This helps automate workflows and store conversation insights for further analysis.

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Where to View Analysis

After a conversation ends, you can view extracted insights in:
  • Go to Conversations
  • Open any Call or Chat
  • Navigate to the Analysis tab
  • Alongside:
    • Transcript
    • Node Logs
    • Dynamic Variables
Analysis

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👉 This helps you:

  • Verify output
  • Debug instructions
  • Improve accuracy

Post Conversation Analysis